State Street turns to a scientist to improve its trading and risk strategies.
Want to Improve Your Portfolio? Call a Scientist
Financial services isn’t normally something most of us consider “scientific” — but that’s about to change, thanks to the advent of analytics and machine learning. “Because it’s worked well with pattern recognition,” notes State Street Global Exchange’s Jeffrey Bohn, who is chief science officer for the company, “we think it’s possible to potentially train an algorithm to see the macroeconomic regime we’re currently in.” And the potential benefits of regime identification, adds Bohn, is that it “could vastly improve our risk forecasting.”
In a conversation with MIT SMR’s David Kiron and Sam Ransbotham, associate professor of information systems at the Carroll School of Management at Boston College and guest editor for the Data and Analytics Big Idea Initiative for the MIT Sloan Management Review, Bohn discusses how he is developing better trading and risk strategies for clients using State Street’s proprietary data and analytics.
Do general managers need to improve their data and analytics capabilities?
Senior executives don’t always come with deep experience in data and analytics and don’t take the time or may not have the interest or the capacity to process the analytics that may be quite crucial to their decision making in today’s rapidly changing markets. I think it’s better now than it has been, but there’s still a ways to go.
What has changed or what is changing that’s making it better?
The primary driver is a more proactive regulatory system. If you’re a commercial bank, you now have to pass certain stress tests and follow all sorts of regulations. If you don’t pass them, you can’t pay dividends, so some executives are forcing themselves to learn about analytics because it has real impact on their personal compensation.
The increased focus on risk and the fact that younger executives running financial institutions may be more likely than older executives to have some kind of analytics background are also factors, although there still are not a lot of quants running them.
What additional skills does someone without a quant background need to make good use of the analytics now expected at financial institutions?
One is a grounding in statistics. A lot of people take statistics in college or in their MBA program, but the average understanding of problems, like how you process uncertainty, is not very good. This is a broader problem in our society today.